# Sparsity in neural networks can improve their privacy

arXiv: https://arxiv.org/abs/2304.10553 · HTML: https://mlsanity.com/paper/2304.10553/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 18 of its 21 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Reconciling modern machine learning practice and the bias-variance trade-off](https://mlsanity.com/paper/1812.11118/) (2018)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Pruning Neural Networks at Initialization: Why are We Missing the Mark?](https://mlsanity.com/paper/2009.08576/) (2020)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Privacy Risks of Securing Machine Learning Models against Adversarial Examples](https://mlsanity.com/paper/1905.10291/) (2019)
- [Monarch: Expressive Structured Matrices for Efficient and Accurate Training](https://mlsanity.com/paper/2204.00595/) (2022)
- [TAN Without a Burn: Scaling Laws of DP-SGD](https://mlsanity.com/paper/2210.03403/) (2022)
- [Membership Inference Attacks and Defenses in Neural Network Pruning](https://mlsanity.com/paper/2202.03335/) (2022)
- [Against Membership Inference Attack: Pruning is All You Need](https://mlsanity.com/paper/2008.13578/) (2020)
- [A Blessing of Dimensionality in Membership Inference through Regularization](https://mlsanity.com/paper/2205.14055/) (2022)
- [Privacy-preserving Learning via Deep Net Pruning](https://mlsanity.com/paper/2003.01876/) (2020)
- [Deformable Butterfly: A Highly Structured and Sparse Linear Transform](https://mlsanity.com/paper/2203.13556/) (2022)
- [On the Statistical Complexity of Estimation and Testing under Privacy Constraints](https://mlsanity.com/paper/2210.02215/) (2022)
- [Differential Privacy Meets Neural Network Pruning](https://mlsanity.com/paper/2303.04612/) (2023)

## Built on by (most cited first, 1 of 1)

- [About the Cost of Central Privacy in Density Estimation](https://mlsanity.com/paper/2306.14535/) (2023)

## Abstract (arXiv)

This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature.